The Semantic Web is realized as a huge graph of data and knowledge. The graph's nodes include literal values, individuals, and ontology terms representing classes and properties, and its edges represent statements of facts. Syntactically, an ontology term has two parts, a namespace identifying the ontology defining the ontology term and a local name identifying a term in the ontology. Namespaces act to resolve ambiguity by allowing terms in different ontologies to share the same local name. For example, the class “Party” can be defined in a “politics” ontology, as well as a “recreational activities” ontology. Authoring or querying knowledge on the Semantic Web is made difficult because it requires people to have some knowledge of the ontologies and to manually select ontologies for the concepts they want to describe, select, query, or otherwise use. For example, the term ‘Party’ is defined in over 400 Semantic Web ontologies currently known to the Semantic Web search engine Swoogle. Thus, a person would normally need some knowledge of the various ontologies in order to efficiently and effectively describe, search, select, search or otherwise work with the various ontologies. Additionally, other ontologies with local names such as, for example, “Celebration” and “Organization” may possibly define concepts related to the specific word “Party”.
The Semantic Web was designed with a goal of unambiguously defining and using ontologies to encode data and knowledge on the World Wide Web. Resource Description Framework or RDF (specifications by World Wide Consortium, W3C,) was designed as a metadata data model and is used for modeling information, including Web resources However, it can be difficult to effectively write complex Resource Description Framework (RDF) statements and queries for the Semantic Web since doing so requires at least some familiarity with the appropriate ontologies and the terms defined by those ontologies.
Systems are desired to automatically map a set of ordinary natural language or vocabulary words to a set of corresponding appropriate ontology terms on the Semantic Web. Such systems should be able to operate on various data sources (including semantically-deprived data sources), ontology terms and ontology correlation statistics, resolve contextual meanings, and provide an efficient mechanism for finding the most suitable ontology context as well as appropriate ontology terms, given an input of natural language or vocabulary words. Desired are systems and methods that provide a mechanism for a user to use natural language or vocabulary as an input and that provide the user with appropriate Semantic Web ontology terms corresponding to the input of the natural language or vocabulary words.